namespace caffe {
+/**
+ * @brief Connects Layer%s together into a directed acyclic graph (DAG)
+ * specified by a NetParameter.
+ *
+ * TODO(dox): more thorough description.
+ */
template <typename Dtype>
class Net {
public:
explicit Net(const string& param_file);
virtual ~Net() {}
- // Initialize a network with the network parameter.
+ /// @brief Initialize a network with a NetParameter.
void Init(const NetParameter& param);
- // Run forward with the input blobs already fed separately. You can get the
- // input blobs using input_blobs().
+ /**
+ * @brief Run Forward with the input Blob%s already fed separately.
+ *
+ * You can get the input blobs using input_blobs().
+ */
const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL);
- // The From and To variants of Forward and Backward operate on the
- // (topological) ordering by which the net is specified. For general DAG
- // networks, note that (1) computing from one layer to another might entail
- // extra computation on unrelated branches, and (2) computation starting in
- // the middle may be incorrect if all of the layers of a fan-in are not
- // included.
+ /**
+ * The From and To variants of Forward and Backward operate on the
+ * (topological) ordering by which the net is specified. For general DAG
+ * networks, note that (1) computing from one layer to another might entail
+ * extra computation on unrelated branches, and (2) computation starting in
+ * the middle may be incorrect if all of the layers of a fan-in are not
+ * included.
+ */
Dtype ForwardFromTo(int start, int end);
Dtype ForwardFrom(int start);
Dtype ForwardTo(int end);
- // Run forward using a set of bottom blobs, and return the result.
+ /// @brief Run forward using a set of bottom blobs, and return the result.
const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
Dtype* loss = NULL);
- // Run forward using a serialized BlobProtoVector and return the result
- // as a serialized BlobProtoVector
+ /**
+ * @brief Run forward using a serialized BlobProtoVector and return the
+ * result as a serialized BlobProtoVector
+ */
string Forward(const string& input_blob_protos, Dtype* loss = NULL);
- // The network backward should take no input and output, since it solely
- // computes the gradient w.r.t the parameters, and the data has already
- // been provided during the forward pass.
+ /**
+ * The network backward should take no input and output, since it solely
+ * computes the gradient w.r.t the parameters, and the data has already been
+ * provided during the forward pass.
+ */
void Backward();
void BackwardFromTo(int start, int end);
void BackwardFrom(int start);
return loss;
}
- // Updates the network weights based on the diff values computed.
+ /// @brief Updates the network weights based on the diff values computed.
void Update();
- // For an already initialized net, ShareTrainedLayersWith() implicitly copies
- // (i.e., using no additional memory) the already trained layers from another
- // Net.
+ /**
+ * @brief For an already initialized net, implicitly copies (i.e., using no
+ * additional memory) the pre-trained layers from another Net.
+ */
void ShareTrainedLayersWith(Net* other);
// For an already initialized net, CopyTrainedLayersFrom() copies the already
// trained layers from another net parameter instance.
+ /**
+ * @brief For an already initialized net, copies the pre-trained layers from
+ * another Net.
+ */
void CopyTrainedLayersFrom(const NetParameter& param);
void CopyTrainedLayersFrom(const string trained_filename);
- // Writes the net to a proto.
+ /// @brief Writes the net to a proto.
void ToProto(NetParameter* param, bool write_diff = false);
- // returns the network name.
+ /// @brief returns the network name.
inline const string& name() { return name_; }
- // returns the layer names
+ /// @brief returns the layer names
inline const vector<string>& layer_names() { return layer_names_; }
- // returns the blob names
+ /// @brief returns the blob names
inline const vector<string>& blob_names() { return blob_names_; }
- // returns the blobs
+ /// @brief returns the blobs
inline const vector<shared_ptr<Blob<Dtype> > >& blobs() { return blobs_; }
- // returns the layers
+ /// @brief returns the layers
inline const vector<shared_ptr<Layer<Dtype> > >& layers() { return layers_; }
- // returns the bottom and top vecs for each layer - usually you won't need
- // this unless you do per-layer checks such as gradients.
+ /**
+ * @brief returns the bottom vecs for each layer -- usually you won't
+ * need this unless you do per-layer checks such as gradients.
+ */
inline vector<vector<Blob<Dtype>*> >& bottom_vecs() { return bottom_vecs_; }
+ /**
+ * @brief returns the top vecs for each layer -- usually you won't
+ * need this unless you do per-layer checks such as gradients.
+ */
inline vector<vector<Blob<Dtype>*> >& top_vecs() { return top_vecs_; }
inline vector<vector<bool> >& bottom_need_backward() {
return bottom_need_backward_;
inline vector<Dtype>& blob_loss_weights() {
return blob_loss_weights_;
}
- // returns the parameters
+ /// @brief returns the parameters
inline vector<shared_ptr<Blob<Dtype> > >& params() { return params_; }
- // returns the parameter learning rate multipliers
+ /// @brief returns the parameter learning rate multipliers
inline vector<float>& params_lr() { return params_lr_; }
inline vector<float>& params_weight_decay() { return params_weight_decay_; }
const map<string, int>& param_names_index() { return param_names_index_; }
- // Input and output blob numbers
+ /// @brief Input and output blob numbers
inline int num_inputs() { return net_input_blobs_.size(); }
inline int num_outputs() { return net_output_blobs_.size(); }
inline vector<Blob<Dtype>*>& input_blobs() { return net_input_blobs_; }
inline vector<Blob<Dtype>*>& output_blobs() { return net_output_blobs_; }
inline vector<int>& input_blob_indices() { return net_input_blob_indices_; }
inline vector<int>& output_blob_indices() { return net_output_blob_indices_; }
- // has_blob and blob_by_name are inspired by
- // https://github.com/kencoken/caffe/commit/f36e71569455c9fbb4bf8a63c2d53224e32a4e7b
- // Access intermediary computation layers, testing with centre image only
bool has_blob(const string& blob_name);
const shared_ptr<Blob<Dtype> > blob_by_name(const string& blob_name);
bool has_layer(const string& layer_name);
void set_debug_info(const bool value) { debug_info_ = value; }
// Helpers for Init.
- // Remove layers that the user specified should be excluded given the current
- // phase, level, and stage.
+ /**
+ * @brief Remove layers that the user specified should be excluded given the current
+ * phase, level, and stage.
+ */
static void FilterNet(const NetParameter& param,
NetParameter* param_filtered);
+ /// @brief return whether NetState state meets NetStateRule rule
static bool StateMeetsRule(const NetState& state, const NetStateRule& rule,
const string& layer_name);
protected:
// Helpers for Init.
- // Append a new input or top blob to the net.
+ /// @brief Append a new input or top blob to the net.
void AppendTop(const NetParameter& param, const int layer_id,
const int top_id, set<string>* available_blobs,
map<string, int>* blob_name_to_idx);
- // Append a new bottom blob to the net.
+ /// @brief Append a new bottom blob to the net.
int AppendBottom(const NetParameter& param, const int layer_id,
const int bottom_id, set<string>* available_blobs,
map<string, int>* blob_name_to_idx);
+ /// @brief Append a new parameter blob to the net.
void AppendParam(const NetParameter& param, const int layer_id,
const int param_id);
- // Helpers for displaying debug info.
+ /// @brief Helper for displaying debug info in Forward.
void ForwardDebugInfo(const int layer_id);
+ /// @brief Helper for displaying debug info in Backward.
void BackwardDebugInfo(const int layer_id);
+ /// @brief Helper for displaying debug info in Update.
void UpdateDebugInfo(const int param_id);
- // Function to get misc parameters, e.g. the learning rate multiplier and
- // weight decay.
+ /// @brief Get misc parameters, e.g. the LR multiplier and weight decay.
void GetLearningRateAndWeightDecay();
- // Individual layers in the net
+ /// @brief Individual layers in the net
vector<shared_ptr<Layer<Dtype> > > layers_;
vector<string> layer_names_;
map<string, int> layer_names_index_;
vector<bool> layer_need_backward_;
- // blobs stores the blobs that store intermediate results between the
- // layers.
+ /// @brief the blobs storing intermediate results between the layer.
vector<shared_ptr<Blob<Dtype> > > blobs_;
vector<string> blob_names_;
map<string, int> blob_names_index_;
vector<bool> blob_need_backward_;
- // bottom_vecs stores the vectors containing the input for each layer.
- // They don't actually host the blobs (blobs_ does), so we simply store
- // pointers.
+ /// bottom_vecs stores the vectors containing the input for each layer.
+ /// They don't actually host the blobs (blobs_ does), so we simply store
+ /// pointers.
vector<vector<Blob<Dtype>*> > bottom_vecs_;
vector<vector<int> > bottom_id_vecs_;
vector<vector<bool> > bottom_need_backward_;
- // top_vecs stores the vectors containing the output for each layer
+ /// top_vecs stores the vectors containing the output for each layer
vector<vector<Blob<Dtype>*> > top_vecs_;
vector<vector<int> > top_id_vecs_;
- // Vector of weight in the loss (or objective) function of each net blob,
- // indexed by blob_id.
+ /// Vector of weight in the loss (or objective) function of each net blob,
+ /// indexed by blob_id.
vector<Dtype> blob_loss_weights_;
vector<int> param_owners_;
vector<string> param_display_names_;
vector<pair<int, int> > param_layer_indices_;
map<string, int> param_names_index_;
- // blob indices for the input and the output of the net
+ /// blob indices for the input and the output of the net
vector<int> net_input_blob_indices_;
vector<int> net_output_blob_indices_;
vector<Blob<Dtype>*> net_input_blobs_;
vector<Blob<Dtype>*> net_output_blobs_;
string name_;
- // The parameters in the network.
+ /// The parameters in the network.
vector<shared_ptr<Blob<Dtype> > > params_;
- // the learning rate multipliers
+ /// the learning rate multipliers
vector<float> params_lr_;
- // the weight decay multipliers
+ /// the weight decay multipliers
vector<float> params_weight_decay_;
- // The bytes of memory used by this net
+ /// The bytes of memory used by this net
size_t memory_used_;
- // Whether to compute and display debug info for the net.
+ /// Whether to compute and display debug info for the net.
bool debug_info_;
DISABLE_COPY_AND_ASSIGN(Net);
projects / platform / upstream / caffe.git / commitdiff